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Manuel Weber, Eshref Januzaj, Peter Mandl
Munich University of Applied Sciences,Department of Computer Science and Mathematics
Data-Driven Analysis of Building Use
IEECB & SC 2020, December 1-2
Agenda
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
2
• NuData Campus Project
• Idea: Data Mining for Building Analysis
• Knowledge Generation from…
‒ Static Data
‒ Dynamic Data
NuData Campus Project
• 3-year research project
• Analysis of energy demand in complex building structures
• Case study in a German university building
IEECB&SC‘20 / Weber, Januzaj, MandlData-Driven Analysis of Building Use
3
Faculty building of the department of Mathematics and Computer Science in Munich, Germany
Idea: Data Mining for Building Analysis
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
4
(x1, x2, x3, x4, x5) Parametrisation Data Mining
Knowledge
Idea: Data Mining for Building Analysis
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
5
Data
Inte
gra
tion
Idea: Data Mining for Building Analysis
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
6
Data
Inte
gra
tion
Idea: Data Mining for Building Analysis
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
7
Data
Inte
gra
tion
Idea: Data Mining for Building Analysis
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
8
Data
Inte
gra
tion
Idea: Data Mining for Building Analysis
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
9
Pa
tte
rn S
ea
rch
(Clu
ste
rin
g)
Data
Inte
gra
tion
Static Data – Clustering of Office Rooms by Area and Perimeter
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
10
Area
Pe
rim
ete
r
Static Data – Clustering of Office Rooms by Area and Perimeter
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
11
Area
Pe
rim
ete
r
Static Data – Room Types
12
Offices
Classrooms
Bathrooms
Labs
Ro
om
usa
ge
acco
rdin
gto
DIN
27
7
Number of rooms
Live and Stay
Storage Rooms
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
Static Data – Classification of Rooms by Area and Perimeter
13
Offices
Classrooms
Bathrooms
Labs
Ro
om
usa
ge
acco
rdin
gto
DIN
27
7
Number of rooms
Classifier Accuracy
SupportVectorClassifier 0.7578
KNeighborsClassifier 0.8323
DecisionTreeClassifier 0.8323
RandomForestClassifier 0.8447
Live and Stay
Storage Rooms
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
Static Data – Classification of Rooms by Area and Perimeter
14
NF2 Office
NF5 Classroom NF1 Live and Stay
NF3 Laboratory
NF7 Bathroom
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
Dynamic Data – Wifi Access Data
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
15
Contact Fair Lectures Not in use
eduroam public WiFi
Dynamic Data – Room Types
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
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Number of rooms
Ro
om
usage
accord
ing
toD
IN277
Dynamic Data – Example: Convolutional Neural Network (CNN) Classification
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
17
Co
nv1
D
Co
nv1D
MaxP
oo
lin
g1D
Fla
tten
Fu
lly
Co
nn
ecte
dL
ayer
Fu
lly
Co
nn
ecte
dL
ayer
Ou
tpu
t L
ayer
Convolutional Neural Network
0 Teaching Room
1 Study Room
Dynamic Data – Example: Convolutional Neural Network (CNN) Classification
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
18
Co
nv1
D
Co
nv1D
MaxP
oo
lin
g1D
Fla
tten
Fu
lly
Co
nn
ecte
dL
ayer
Fu
lly
Co
nn
ecte
dL
ayer
Ou
tpu
t L
ayer
Convolutional Neural Network
0 Teaching Room
1 Study Room
Accuracy 0.7257 (std. 0.0229)
ROC AUC 0.6455 (std. 0.0126)
PR AUC 0.2898 (std. 0.0166)
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
19
Thank you for your attention!
Backup: DIN 277 - Categorization of the Net Floor Area in Buildings
20
Net Floor Area (NF)
Effective Area (NF 1-7) Functional Area (NF 8) Common Area (NF 9)
NF 1 Live & Stay
NF 2 Office Work
NF 3Production etc.
NF 4 Storage
NF 5 Education &
Culture
NF 6 Health & Care
NF 7Other
711 Bathroom719 Cleaning Room
…
513 Lecture Hall521 Classroom523 Practice Room533 Media Room535 Technical Practice Room
…
322 Metal Workshop323 Electrical Workshop353 Chem./Techn. Laboratory382 Kitchen
…
211 Office Room231 Meeting Room
…
120 Common Room123 Children‘s
Playroom135 Quiet Room152 Dining Hall
…
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
411 Storage Room…
612 First Aid Room
Backup: Classifier
21
Support Vector Machine K-Nearest Neighbors
Decision Tree Random Forest
k=3
perimeter > 20 ?
area > 25 ?
area < 100 ?
…
…
…
Voting
Dataset
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
Backup: Area Under the Curve (AUC)
22
ROC AUCReceiver-Operating-Characteristic
PR AUCPrecision-Recall
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
1
10False Positive Rate
Tru
e P
ositiv
e R
ate
1
10Recall
Pre
cis
ion
AUC=1 AUC=1
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =Σ True positives
Σ Predicted positives
𝑅𝑒𝑐𝑎𝑙𝑙 =Σ True positives
Σ Total positivesFalse Positive Rate =
Σ False positives
Σ Total negatives
True Positive Rate =Σ True positives
Σ Total positives
Backup: Building Data Integration
23
Dynamic Data
CAFM System Wifi Access Points
Building Plans (CAD)Sensor Data
Integrated
Building
Data
Static Data
Data Mining
IEECB&SC‘20 / Weber, Januzaj, Mandl
Data-Driven Analysis of Building Use
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